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emphasizes the role of systems biology in medical/clinical applications. With the advent of new technologies, the " omics " explosion (i.e., next generation sequencing) and the induced changes from data-poor to data-rich applications (for instance related to high-content imaging, physiology , and structural biology) have established the necessity of a(More)
The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields.(More)
A paragraph from the highlights of "Transcriptomics: Throwing light on dark matter" by L. Flintoft (Nature Reviews Genetics 11, 455, 2010), says: "Reports over the past few years of extensive transcription throughout eukaryotic genomes have led to considerable excitement. However, doubts have been raised about the methods that have detected this pervasive(More)
do we need " networks " in systems medi-cine? Maybe there is not a simple answer. Until recently, scientists from mathematics, physics, statistics, machine learning, computer science, and similar quantitative/ computational disciplines were converging to bioinformatics for known reasons. A main one was to support the experimental and theoretical work of(More)
Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative(More)
Comorbidity represents an extremely complex domain of research. An individual entity, the patient, is the center of gravity of a system characterized by multiple, complex, and interrelated conditions, disorders, or diseases. Such complexity is influenced by uncertainty that is difficult to decipher and is proportional to the number of associated(More)
Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to(More)